The present invention relates to a learning control system, and more specifically to an engine air fuel ratio learning control system.
In a so-called three way catalytic conversion system, in order to enhance the conversion efficiencies of the three harmful exhaust components (CO, HC and NO.sub.x), a conventional air fuel ratio control system performs a feedback control so as to hold the air fuel ratio of the exhaust gas mixture passing through the catalyst in a predetermined narrow range around the theoretical ratio.
Although the base air fuel ratio (which is the air fuel ratio determined by a basic injection pulse T.sub.p determined in accordance with the output of an air flow meter and the engine speed) is set equal to :the theoretical ratio, the air fuel ratio goes out of order for some reason such as a deviation of a flow characteristic of the air flow meter or the fuel injector from a prescribed standard. In this case, the output of an O.sub.2 sensor changes; .fwdarw. a computer varies the fuel injection quantity little by little to reduce the deviation of the air fuel ratio; .fwdarw. the output of the O.sub.2 sensor returns gradually to the normal level; .fwdarw. the air fuel ratio returns gradually to the theoretical ratio. The control system controls the air fuel ratio at or near the theoretical value by repeating this process.
However, it takes more or less time for the feedback control system to return the actual air fuel ratio to the theoretical ratio, and, during that time, a bad condition lingers. Thereafter, the control system can maintain a normal state until a next stop of the engine. When the engine is restarted, however, the control system must repeat the above-mentioned feedback cycle of monitoring the output of the O.sub.2 sensor and adjusting the injection quantity. Namely, the abnormal condition persists for a while each time the engine is started. Moreover, the air fuel ratio remains out of order while the air fuel ratio feedback control is held in abeyance in some engine operating states as in a starting operation, a cold operation in which the temperature of the cooling water is low and a high load operation.
Therefore, some air fuel feedback control systems are devised to have a learning function to improve the response characteristic of the air fuel ratio control ("Jidosha Kogaku (Automotive Engineering)", Jul., 1991, pages 72 .about.74; and Japanese Patent Provisional Publication No. 60-145443).
The control system having this learning function determines a correction quantity (or a learning variable) required for a learning control by monitoring the performance of the feedback correction, and stores the learned data in a storage device which can save the contents even after a turn-off of the engine, as long as a back up power source of a computer is normal. By using this learned data, the control system can start an adequate enriching or leaning corrective action from the beginning when the engine is restarted. Therefore, this control system is free from an undesired transient phenomena. With the learning function, the control system can take an immediate and adequate corrective action, instead of repetition of the feedback cycle resulting in a gradual transient variation of the air fuel ratio, and accordingly, the control system can prevent an out-of-order condition from taking place.
Even in the engine operating conditions in which the control system stops performing the feedback control, the control system can continue the correction based on the learned variable to ensure the desirable air fuel ratio, and keep the behavior of the controlled system in order.
The control system updates the learning variable to a new value by comparing with an old one. This update operation is not correctly performed unless the engine operating conditions remain unchanged. Therefore, the control system performs the update operation only when predetermined stringent requirements (learning conditions) are satisfied. The first requirement is that the feedback correction is operative. Under this condition, the control system is trying to reduce a deviation from the theoretical ratio even though there is some difference in the engine operating conditions. Another requirement is that the output of the oxygen sensor has been sampled a predetermined number of times in the same one of a plurality of learning areas.
An object of the conventional learning function is to smooth the nonuniformity from product to product and to eliminate a deviation of the base air fuel ratio. Therefore, the learning range is narrow (.+-.10%, for example), and the updating speed of the learning variable is relatively fast.
The conventional learning system, however, cannot provide a satisfactory performance when there arises such a severe deviation of the air fuel ratio as to overstep the learning range. When, for example, the air fuel ratio swerves sharply to the rich side because of an accidental failure in a part of the engine fuel system, then the learning variable decreases at a relatively high speed in an effort to bring back the air fuel ratio to the lean side. The conventional learning variable, however, shortly reaches the lower limit of the rather narrow learning range, and stays clingingly at the lower limit, blocking further advance of the learning control. Without the lower limit, the learning variable would be further decreased until an equilibrium is reached. When the deviation of the air fuel ratio is excessive, the learning control does not function properly, and the exhaust performance becomes worse.